Chlorophyll Concentration#
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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys
import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots, add_oni_cat
sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data, download_oni_index
Setup#
Define area of interest
#Area of interest
lon_range = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]
EEZ shapefile
shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)
Download Data#
update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
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base_url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-monthly-v6-0.csv'
dataset_id = 'chlor_a'
if update_data:
date_ini = '1998-01-01T00:00:00Z'
date_end = '2023-12-01T00:00:00Z'
data = download_ERDDAP_data(base_url, dataset_id, date_ini, date_end, lon_range, lat_range)
data_xr = data.set_index(['latitude', 'longitude', 'time']).to_xarray()
data_xr['time'] = pd.to_datetime(data_xr.time)
data_xr = data_xr.coarsen(longitude=2, latitude=2, boundary = 'pad').mean()
data_xr.to_netcdf(op.join(path_data, f'griddap_{dataset_id}.nc'))
else:
data_xr = xr.open_dataset(op.join(path_data, f'griddap_{dataset_id}.nc'))
Analysis#
Average#
ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(),
cmap = 'Greens', vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1),
vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99))
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label='chlorophyll (mg/$m^3$)')
plt.savefig(op.join(path_figs, 'F15_chlorophyll_mean_map.png'), dpi=300, bbox_inches='tight')
Annual average#
data_y = data_xr.resample(time='1YE').mean()
fig = plot_map_subplots(data_y, dataset_id, shp_eez = shp_eez, cmap = 'Greens', vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1),
vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99), cbar = 1)
Annual anomaly#
data_an = data_y - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.1, vmax=.1, cbar = 1)
Average over area#
dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(),
'var' : dataset_id, 'ax' : 1, 'label' : 'Chlorophyll - MEAN AREA'},]
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = 'Chlorophyll (mg/m3)');
fig.write_html(op.join(path_figs, 'F15_chlorophyll_mean_trend.html'), include_plotlyjs="cdn")
d_p = data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe()
Timeseries at a given point#
loc = [7.37, 134.7]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(),
'var' : dataset_id, 'ax' : 1, 'label' : f'Chlorophyll at [{loc[0]}, {loc[1]}]'},]
ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()
<matplotlib.legend.Legend at 0x18baee660>
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = 'Chlorophyll (mg/m3)');
ONI index analysis#
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
df1 = add_oni_cat(df1, lims = lims)
df1['ONI'] = df1['oni_cat']
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()
Average#
fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'Greens',
vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1),
vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99),
sub_plot= [1, 3], figsize = (20, 9), cbar = True, cbar_pad = 0.1,
titles = ['La Niña', 'Neutral', 'El Niño'],)
plt.savefig(op.join(path_figs, 'F15_chlorophyll_ENSO.png'), dpi=300, bbox_inches='tight')
Anomaly#
data_an = data_oni - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.05, vmax=.05,
sub_plot= [1, 3], figsize = (20, 9), cbar = True, cbar_pad = 0.1,
titles = ['La Niña', 'Neutral', 'El Niño'],)
